Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

TL;DR

This paper demonstrates how phase-coded spike patterns can be stored and selectively replayed in a leaky integrate-and-fire neural network, revealing different regimes of activity and a robust coding scheme based on phase, rate, and frequency.

Contribution

It introduces a novel associative memory model for phase-coded patterns in LIF networks, with a new order parameter and analysis of robustness and storage capacity.

Findings

Different replay regimes depend on network excitability.

Phase relationships are preserved despite noise and heterogeneity.

Frequency and rate can be modulated independently during replay.

Abstract

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at differentctime scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stablecprecise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units.